ai.djl.modality.rl.agent

Class EpsilonGreedy

java.lang.Object

ai.djl.modality.rl.agent.EpsilonGreedy

All Implemented Interfaces:

RlAgent

public class EpsilonGreedy
extends java.lang.Object
implements RlAgent

The EpsilonGreedy is a simple exploration/excitation agent.

It helps other agents explore their environments during training by sometimes picking random actions.

If a model based agent is used, it will only explore paths through the environment that have already been seen. While this is sometimes good, it is also important to sometimes explore new paths as well. This agent exhibits a tradeoff that takes random paths a fixed percentage of the time during training.

Constructor Summary

Constructors

Constructor and Description
EpsilonGreedy(RlAgent baseAgent, Tracker exploreRate) Constructs an EpsilonGreedy.

Method Summary

Modifier and Type	Method and Description
NDList	chooseAction(RlEnv env, boolean training) Chooses the next action to take within the RlEnv.
void	trainBatch(RlEnv.Step[] batchSteps) Trains this RlAgent on a batch of RlEnv.Steps.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

EpsilonGreedy

public EpsilonGreedy(RlAgent baseAgent,
                     Tracker exploreRate)

Constructs an EpsilonGreedy.

Parameters:

baseAgent - the (presumably model-based) agent to use for exploitation and to train

exploreRate - the probability of taking a random action

Method Detail

chooseAction

public NDList chooseAction(RlEnv env,
                           boolean training)

Chooses the next action to take within the RlEnv.

Specified by:

chooseAction in interface RlAgent

Parameters:

env - the current environment

training - true if the agent is currently traning

Returns:

the action to take

trainBatch

public void trainBatch(RlEnv.Step[] batchSteps)

Trains this RlAgent on a batch of RlEnv.Steps.

Specified by:

trainBatch in interface RlAgent

Parameters:

batchSteps - the steps to train on